No abstract

Synchro-modal supply chain eco-NET (SYNCHRO-NET) is a Horizon 2020 European research project aimed at overcoming the stress due to the increasing transportation distances, the higher complexity, and the vulnerability of international supply chains. In order to foster sustainability, quality, and reliability of such supply chains, SYNCHRO-NET mainly exploits the impact and the possible benefits coming from slow/smart-steaming and synchro-modality transportation strategies, and the related business models. The aim of this paper is to summarize and disseminate the main developments and scientific contributions coming from the project. In particular, the working core of the SYNCHRO-NET solution is an integrated and cloud-based eco-system of optimization and simulation software modules that supports stakeholders’ decisions in freight transportation and logistics management at strategic, operational, and real-time levels. The platform has achieved a high grade of automation in several processes to overcome all the issues related to human-based operations. The efficiency and effectiveness of the overall platform have been tested on three case studies considering pan-European and regional trade lanes, as well as commercial activities between the Far East and European ports. The project results and outputs also allow us to analyze barriers and opportunities of the approach, industrial and academic developments, and relations with emerging technologies.

No abstract

Slow steaming, i.e., the possibility to ship vessels at a significantly slower speed than their nominal one, has been widely studied and implemented to improve the sustainability of long-haul supply chains. However, to create an efficient symbiosis with the paradigm of synchromodality, an evolution of slow steaming called smart steaming is introduced. Smart steaming is about defining a medium speed execution of shipping movements and the real-time adjustment (acceleration and deceleration) of traveling speeds to pursue the entire logistic system’s overall efficiency and sustainability. For instance, congestion in handling facilities (intermodal hubs, ports, and rail stations) is often caused by the common wish to arrive as soon as possible. Therefore, smart steaming would help avoid bottlenecks, allowing better synchronization and decreasing waiting time at ports or handling facilities. This work aims to discuss the strict relationships between smart steaming and synchromodality and show the potential impact of moving from slow steaming to smart steaming in terms of sustainability and efficiency. Moreover, we will propose an analysis considering the pros, cons, opportunities, and risks of managing operations under this new policy.

The transshipment location–allocation problem consists of locating transshipment facilities (e.g., intermodal hubs) of a transportation network and allocating freight flows through them, from several origins to several destinations, to satisfy demand and supply constraints. The objective is to maximize the total net transportation utility given by the total shipping utility minus the total cost to locate the facilities. Moreover, flow synchronization at the facilities must also be ensured. Unfortunately, the flow synchronization depends on a broad set of unknown events, which could cause both unexpected reductions of the facility capacity and uncertain utility of handling operations. In this paper, we first want to evaluate how uncertainty on facility capacity and handling operations utility affects the transshipment location–allocation problem in terms of complexity, net gain, and optimal solutions. Moreover, we extend the problem from a single to a multi‐period setting to have a wider view of future scenarios realizations and consequently synchronize the flows by using different facilities on different periods. We propose a two‐stage stochastic programming formulation with recourse and analyze, over a ground set of instances, some well‐known economic indicators to derive managerial insights on the importance of addressing uncertainty for the problem. Finally, given the computational burden of solving the deterministic equivalent problem, we propose several heuristics based on progressive hedging and test their performance.